FDIA detection algorithms are classified into two main categories as model-based methods and data-driven methods [1]}.
In model-based methods, a system model is built and its parameters are estimated. They do not require a historical dataset since they do not include any separate system to be trained.
However, manual threshold optimization steps, high detection delays, and scalability issues limit their applicability for real time analysis.
In contrast, data-driven methods eliminate the manual tuning steps, increase the scalability of the algorithm for attack detection, and reduce the detection time at the expense of a training process which need a historical dataset [1]}.
